Case study: Challenges faced by EMIF in utilising the OMOP CDM

Johan van der Lei Erasmus Medical Center

Rotterdam

Outline

• Scaffolding • EMIF and a CDM • EMIF and the OMOP CDM • Ongoing activities/challenges

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Information

• Syntactic: “grammar” • Semantic: “meaning” • Pragmatic: “consequences”

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syntactic

semantic

pragmatic

CDM Analytical tools Mapped data Source Data

Regulatory context

Views on information

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syntactic

semantic

pragmatic

Views on information

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CDM…

• On the syntactic level • Multiple solutions possible

• Models are dynamic • Debate often: semantic and pragmatic • But that discussion is often independent of a

specific model

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• The question which CDM to use is probably not the right question……

From medical informatics perspective:

syntactic

semantic

pragmatic • Is a ‘socio-technical’ construct • Can only be understood in

context • Defines roles and responsibilities

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Project overview ACADEMIC PARTNERS

SME PARTNERS EFPIA PARTNERS

PATIENT ORGANISATION

14 European countries combining 57 partners €56 million worth of resources

3 projects in one

5 year project (2013–2017)

37

10 9

1

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Biomarker discovery Predictive modelling Disease insight generation

(opportunity identification)

Trial design and feasibility analysis Electronic health record

(EHR)-facilitated recruitment Prospective cohort

selection

Analysis of treatment pathways Collection of clinical and

economic evidence Ongoing efficiency and

safety monitoring

Why is EMIF needed? Potential applications of Real World Data

Discovery Development Launch/ Post-Launch

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EMIF Setting

• Data from very diverse sources • Population based • Hospital based • Disease specific cohorts • Biobanks

• Diverse data • Broad spectrum of research questions • Overall purpose: facilitate re-use of data

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EMIF and CDM Challenges

• Clear need for a CDM • Broad spectrum of coding schemes, languages, and

settings • Need to store ALL source data including source

vocabularies • Possibility to escape/refine to study-specific solutions • Reproducible research: Open, Transparent, Source

data, Mappings, Analytical tools • Flexibility in role transfer (e.g., study coordinator) • Multiple technical infrastructures

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EMIF and OMOP CDM: why?

• No silver bullet… • but not yet another model !!! • Diversity of the EU setting: Support for

Standardized Vocabularies • Not limited to specific analytical use case • Open source • Multiple platforms • OHDSI

• Open collaborative • Growing in EU

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EMIF Databases being mapped to the OMOP-CDM

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ETL requires multi-disciplinary team

Medical knowledge

ETL Development

Database / Infrastructure management

Project Coordination

CDM Knowledge (Local) Data knowledge

Local

EMIF

Depending on preferences & available skills, EMIF can take on

different roles

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Tools supporting the process

White rabbit Rabbit in a hat github

usagi

Achilles

Analyze Data Source

Evaluate Infrastructure

Define Mapping

Logic

Develop Mappings

(ETL) Test ETL Deploy ETL Acceptance

Testing / QA

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16 Nov 2017 EMIF Introduction

Current Challenges: ETL

The following factors were found to be most impactful on overall speed and quality of the ETL: 1. Source Database research readiness: The ‘quality’ of the input

data structure – and the availability of internal knowledge on how the database is defined- are the primary driver of efficiency and quality of the CDM Mapping.

2. Strong project management: superior results in terms of quality

and speed can be achieved when resources are allocated and active project management is executed.

3. Vocabulary mappings: establishing the vocabulary mappings is the most resource intensive step. It’s recommended to set realistic goals with associated timings (e.g. map the top 20% of lab tests, covering 80% of all occurrences).

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Evaluation of translation: Structural Mapping

Did all my source data end up in the CDM?

Prevalence Source

Pre

vale

nce

CD

M

• Can be very good reason for differences: business rules assessment • Iterative process to optimize the ETL • No structural CDM limitations encountered so far

Lost in translation?

Evaluation of translation: Vocabulary Mapping

• High data coverage. • Term coverage is further improved by extending the Standard

Vocabularies, e.g. RxNorm-Extension to accommodate European Drug market

IPCI Database Example

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Current EMIF CDM Activities

• Replication of existing EMIF Use Case(s) on CDM • Contribute to vocabulary extension • Contribute to tool development • Training of stakeholders in using the CDM and

OHDSI Tools • Initiate and participate in OHDSI Network studies

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Example: Treatment Pathway Study

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Hripcsak G. et al. Characterizing treatment pathways at scale using the OHDSI network. PNAS 2016 113 (27) 7329-7336;

IPCI: Type 2 Diabetes

EMIF will run this study on CDM databases in

Europe

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Final Remarks

• EMIF will intensify its participation in the OHDSI network by supporting the European OHDSI initiative (www.ohdsi-europe.org) coordinated by Erasmus MC

• EMIF is in the process of a sustainability assessment to

support and use the data network post EMIF We believe that the adoption of the OMOP-CDM and the active OHDSI community will enable transparent

and reproducible research at an unprecedented scale in Europe

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